Evaluation and analysis of the environmental noise of Messina, Italy

In this paper, the results of a study on the environmental noise pollution of the city of Messina (Italy) are presented. The investigation has included a preliminary classification of the territory in six acoustically homogeneous areas according to Italian noise regulations. On the basis of the resultant acoustic zoning 35 sites were selected for an experimental survey. This last has been carried out by extensive measurements of the main indexes for noise pollution (L_eq, L₁, L₁₀, L₅₀, L₉₀, L₉₉) and of the traffic flow and composition. Results indicate that: (a) main roads of Messina are overloaded by traffic flow during day-time period and that in all the examined sites daily average sound levels due to road traffic exceed environmental standards by about 10 dBA; (b) environmental noise exhibits a certain degree of spatial variance resulting primarily from the peculiar geo-morphological structure of the town and from the transport infrastructure and (c) more than 25% of residents should be highly disturbed by road traffic noise.     

Noise mapping at different stages of a freeway redevelopment project – A case study in Brazil

In economically developing countries such as Brazil, India and China, rising levels of noise pollution are associated with the accelerated growth of cities and the increasing circulation of automotive vehicles. This paper presents the results of an acoustic evaluation conducted in areas adjacent to federal highway BR-116, part of which lies within the urban limits of the city of Curitiba in southern Brazil. In situ measurements were taken of the noise levels, from which noise maps were drawn in different stages of the implementation of the road restructuring project called the Green Line. After calibration, a computational model was used to evaluate an operational scenario of the highway in the future. The results of the mappings were compared with reference noise emission values established by municipal legislation. The maps revealed the existence of noise pollution in the urban stretch of the federal highway in all the scenarios [L_Aeq > 65 dB(A)]. Efforts to control environmental noise in cities are aided by computational models for urban planning. These models are extremely helpful for environmental management and decision-making by public authorities for solutions to potential environmental risks, as is the case of urban noise.     

A study of noise in CNG driven modes of transport in Delhi

Measurements of noise and its spectral characteristics were made inside various types of transport running on Compressed Natural Gas (CNG) fuel in Delhi. Noise indices L₁₀, L₅₀, L₉₀ and L_eq were estimated from the measured noise levels for vehicles in neutral gear, slow speed (speed ?20 km/h) and under free flow (speed ?30 km/h) conditions. It is found that background levels, when averaged over all speeds, are maximum in Rural Transport Vehicles (RTV) followed by Buses, Auto-rickshaws and Taxis. With increase in the speed, noise levels are appreciably enhanced except in the case of auto-rickshaws where the increase is moderate. The spectral distributions of noise inside vehicles obtained at 1-octave band frequencies, show a rather similar nature. The study reveals significantly lower noise levels inside CNG driven public modes of transport compared to those found in an earlier survey inside diesel and petrol driven vehicles.     

Street categorization for the estimation of day levels using short-term measurements

Day time noise level computation by traffic noise model requires realistic traffic data that is hardly ever available for every single street, therefore direct noise levels measurements may be the alternative. However, direct, continuous measurement of the day time noise level (equivalent level from 7 am to 7 pm) is an expensive strategy if a whole city must be assessed. To overcome this situation, short-term measurements of variable duration are often taken in order to estimate the day time noise level (L_d) because they are less expensive and resource demanding than continuous measurements over a whole day or more. From a set of continuous measurements over more than 48 h in 137 streets of nine different cities, the present work quantifies the error committed when using short-term noise measurements as a function of interval length measurement. To enhance estimation accuracy, both a street categorization and a temporal categorization have been attempted: in first place, it is found that the difference between the short-term noise level and L_d follows a different distribution for main roads than for ordinary streets, therefore the error committed depends on the street category. To be specific, when estimating L_d from a 15 min short time measurement the error committed would be ±2 dB with a percent population coverage of 90%, in the case of main streets and in the case of ordinary streets the same error range covers 72.5% of the population. In second place, if the measurement range time is restricted (temporal categorization) from 9 am to 1 pm and from 2 pm to 5 pm in the case of ordinary streets, the percent population coverage increases to 90% when the error range is ±3 dB. When referring to main streets, the measurement range of time is limited from 10 am to 5 pm to obtain an increase of 2% of the percent population coverage when the error is ±1 and ±2 dB.     

Nocturnal road traffic noise assessment and sleep research: The usefulness of different timeframes and in- and outdoor noise measurements

In this paper, we investigated the procedure of noise assessment in the study of nocturnal noise exposure during sleep in the home situation. The use of two different timeframes (fixed from 11 PM to 07 AM versus personal “Time in Bed” period) was explored as well as the relation between indoor and outdoor noise levels and between the actual and estimated noise levels. Noise recordings were performed inside and outside the bedroom of 24 subjects living in high density road traffic noise areas in the Brussels’ Capital Region during seven consecutive days. Indoor and outdoor noise indicators L_Aeq, L_Amax and individual noise events were analysed. Subjects completed a daily sleep log in which the Time in Bed period was assessed. The results indicate that, for outdoor noise assessment, the use of an average L_Aeq might not be sufficient to reflect well the noise levels during the sleep period. For indoor measurements, significant differences were found in the comparison between both timeframes (L_Aeq: T = 16; p < .001). Considering the relation between indoor and outdoor measurements, low correlations (r = .49; p < .001) were found even when the location of the bedroom as a mediating factor was accounted for (street side; r = .52; p < .001). Therefore, from our study, caution is needed when relying on outdoor noise measurements for the evaluation of sleep disturbances. Furthermore, one needs to be aware of the weak correspondence between indoor and outdoor noise levels in the discussion of what a harmonized noise indicator for the evaluation of noise exposure and sleep disturbances should consist of.     

The effect of the number of aircraft noise events on sleep quality

BackgroundBoth the WHO and the EC recommend the use of L_night as the primary indicator for sleep disturbance. Still, a key question for noise policy is whether the prediction of sleep quality could be improved by taking the number of events into account in addition to L_night.ObjectivesThe current paper investigates the association between sleep quality and the number of aircraft noise events. The first aim of this study was to investigate whether, for the purpose of predicting sleep quality measured by motility, the nummer of events is adequately represented in L_night for the purpose of predicting sleep quality measured by motility. The second aim was to investigate whether the number of events at a given L_night has an additional predictive value. In addition, it was explored whether the total number of events should be taken into account for the production of sleep quality, or only the number of events exceeding a certain sound pressure level.MethodsThis study is based on data of a field study among 418 people living within a range of 20 km from Amsterdam Airport Schiphol. The data from this study are well suited for this purpose, since for every subject both the number and the exposure level of events are available. Sleep quality was measured by motility, derived from actimeters worn on the wrist, and by self-reported sleep quality scored on a 11-point scale. Mixed linear regression models were built in a stepwise manner to predict sleep quality during a sleep period time.ResultsThe results show that, given a certain equivalent noise level, additional information on the overall number of events does not improve the prediction of sleep quality. However, the number of events above L_Amax of 60 dB was related to an increase in mean motility, indicating lower sleep quality. No effect of number of events was found on self-reported sleep quality.ConclusionsThis study suggests that the number of events is more or less adequately represented in L_night and only the number of high noise level events may have additional effects on sleep quality as measured by motility. This may be viewed as an indication that, in addition to L_night, the number of events with a relatively high L_Amax could be used as a basis for protection against noise-induced sleep disturbance.     

Community noise survey of the city of Valdivia, Chile

A noise map of the city of Valdivia is created as a way to evaluate the noise of the city. This is a process that is usually associated with high operative costs. Some statistical techniques have been employed that have allowed the extrapolation of some measured values to assess noise at different times of the year, including “off” days thus reducing time and costs. The day-evening-night level L_DEN is used as the rating method to describe long-term annoyance. In conclusion, the noise pollution in the city is widespread throughout most of its streets area, where measured noise values are similar to those commonly observed in cities that do not have mitigation programs and whose road traffic is their principal noise generation source.     

Practical guidance for risk assessment of traffic noise effects on sleep

Environmental noise disturbs sleep and may impair well-being, performance and health. The European Union Directive 2002/49/EC (END) requires member states to generate noise maps and action plans to mitigate traffic noise effects on the population. However, practical guidance for the generation of action plans, i.e. for assessing the effects of traffic noise on sleep, is missing. Based on the current literature, we provide guidance on hazard identification, exposure assessment, exposure-response relationships and risk estimation: there is currently no consensus on both exposure and outcome variables that describe traffic noise effects on sleep most adequately. END suggests the equivalent noise level L_night as the primary exposure variable, and our own simulations of single nights with up to 200 noise events based on a field study on the effects of aircraft noise on sleep support using expert consensus L_night ranges (<30, 30-40, 40-55, >55 dB) for risk assessment. However, the precision of risk assessment may be considerably improved by adding information on the number of noise events contributing to L_night. The calculation of L_night should be extended to the shoulder hours of the day if traffic is busy during these periods. More data are needed on the combined effects of different traffic modes.     

Cross-calibration of participatory sensor networks for environmental noise mapping

Participatory measurements appear as a promising technique for performing noise mapping and monitoring. However, the confidence in the quality of raw data collected through participatory measurements controls the faithfulness of the output noise maps. In this paper, a cross-calibration method is proposed, which aims at both selecting the best candidate sensors and improving the furnished raw data. The method rests upon four steps: (i) an outlier detection, (ii) the crowd sensors-based correction, (iii) a fixed sensors-based correction, and (iv) the L_den estimation. The efficiency of the approach for different characteristics of the network of mobile sensors is evaluated on its ability to reconstruct an artificial reference sound field, which consists of the one-month L_10s evolution, on a twenty streets network. The main conclusions are: (i) the systematic errors of the sensors can be efficiently corrected by a cross-calibration method, and thus do not affect the L_den estimation, (ii) the fixed sensor network helps estimating the average error of the network of mobile sensors, (iii) the dispersion in an individual sensor measurements, which is due for example to the operator, stands for a much more critical concern and should be flagged by a rigorous outlier detection method, as the one proposed in this paper, (iv) although individual measures are improved by the proposed cross-calibration, some errors remain on the L_den estimation because of the shortness of the collected samples, (v) increasing the number of sensors does not improve the L_den estimation as long as individual measurements dispersions remain too large.